Big data in the real world opportunities and challenges facing healthcare - v04b - slide share
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The Healthcare system will be target of major disruption more than any other industry in the next 10 years. The Digital economics and increasing demand by consumers for more real time information in order to make better decisions on who they want to "hire" to perform services for them or in their behalf will be the driver of this disruption. Analytics, Big Data and Machine Learning will lay the foundation for the next generation of healthcare yet there are still many challenges to truly revolutionize the healthcare system end to end (Providers, Pharma, Payers)
Transcript of Big data in the real world opportunities and challenges facing healthcare - v04b - slide share
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Eric SchmidtExecutive Chairman, Google
"From the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two
days...and the pace is accelerating."
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Source: Gartner
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Big Data Categories
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Web & SocialMedia Data
Machine-to-Machine Data
Big TransactionData
BiometricData
Human-Generated Data
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The 3 Vs of Big Data
Volume90% of the data in the world
today was created within the last two years
VarietyPeople to people (e.g. social media)
People to machine (e.g. computers, mobile,
medical devices)
Machine to machine (e.g. sensors, GPS, barcode
scanner)
Velocity2.9 emails sent every
second
20 hours of video uploaded every minute
50 million tweets per day
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Industry Shifts in Data
Data is becoming the world’s new natural
resource
The emergence of cloudis transforming IT and
business processes into digital services
Social, mobile and access to data are changing how individuals are understood
and engaged
500 million DVDs worth of data is generated daily
1 trillion connected objects and devices by
2015
80% of the world’s data is unstructured
85% of new software is being built for cloud
25% of the world's applications will be available
in the cloud by 2016
72% of developers say cloud-based services are central to the applications
they are designing
80% of individuals are willing to trade their information for a
personalized offering
84% of millennials say social and user-generated
content has an influence on what they buy
5 minutes: response time users expect once they have
contacted a company via social media
IT Evolution Compared Healthcare
Exponentially
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Implications in Healthcare
Source: http://www.alphasixcorp.com/images/big-data-infograph.jpg
Megatrends Impacting Entire Spectrum of Care
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A Modern Health Care System is on the Horizon, Demanding a Paradigm Shift
FROM TO
One Size Fits All
Fragmented, One Way
Provider Centric
Centralized, Hospital-based
Fragmented, Specialized
Procedure-based
Treating Sickness
Personalized Medicine
Integrated, Two Way
Patient Centric
Decentralized, Community-based
Collaborative, Share Information
Outcomes-based
Preventing Sickness (Wellness)
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Connected Health Ecosystem
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RemoteMonitoring
General Healthcare IT (CIS
and Non-CIS)
Telemedicine mHealth
• Video Diagnostic Consultation
• Remote Doctor/Specialist Services
• Distance Learning/Simulation
• Retail Telehealth• Teleimaging
• Electronic Health Records (EHR)
• Health Information Exchange (HIE)
• Patient Portals• Hosted Cloud Infrastructure
• Home and Disease Management Monitoring
• Activity Monitoring• Diabetes Management• Wellness Programs• Remote Cardiac ECG• PERS• Medication
Management
• Professional Apps• Wellness Apps• Fitness Apps• Texting Informational
Services
Moving to the LeftBenefits of Proactive Mitigation of Disease Risk
Health Status 20 % of Population Generates80% of the Cost
VALUE COST
Healthy/Low Risk
At RiskHighRisk
ChronicDisease
Early Stage
ChronicDisease
Progression
End ofLife Care
Exponential Technologies
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EMPOWERING THE PATIENT
ENABLING THE PHYSICIAN
ENHANCING WELLNESS
CURING THE WELL…BEFORE THEY GET SICK
What Prevents Insurers from Effectively Using Data?
Inability to get to accurate, integrated data that can provide actionable insights.
Lack of a clear strategy and roadmap
Budget and resources
Data fragmentation
System fragmentation
Poor data quality
Data silos across departments
Inadequate analytic tools and skill sets
Overcoming the Gaps
Leadership commitment to data as a strategic asset
Long term commitment to drive health care value
Alignment with enterprise priorities
Dedicated resources to infrastructure and quality
Continuous improvement mindset
Strategic decisions consider data requirements
Operational decisions include data implications
Strategies • Implement a data governance framework
• Engage providers
• Foster competition and transparency
• Bake analytics into training
• Provide for flexibility in information transference
• When possible, choose in-house solutions over vendor-generated solutions
• Create simple, understandable tools such as dashboards for clinicians on the front lines to visualize incoming data.
• Don’t scale up, scale out
• Close the quality loop
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